Linear Models for Regression — Applied Machine Learning in Python

from sklearn.linear_model import ElasticNet
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

param_grid = {'alpha': np.logspace(-4, -1, 10),
              'l1_ratio': [0.01, .1, .5, .6, .7, .8, .9, .95, 1]}

grid = GridSearchCV(ElasticNet(max_iter=10000, normalize=True), param_grid, cv=10)
grid.fit(X_train, y_train)

print(grid.best_params_)
print(grid.best_score_)